A Scheme for Robust Distributed Sensor Fusion Based on Average Consensus

We consider a network of distributed sensors, where each sensor takes a linear
measurement of some unknown parameters, corrupted by independent Gaussian
noises. We propose a simple distributed iterative scheme, based on distributed
average consensus in the network, to compute the maximum-likelihood estimate of
the parameters. This scheme doesn't involve explicit point-to-point message
passing or routing; instead, it diffuses information across the network by
updating each node's data with a weighted average of its neighbors’ data (they
maintain the same data structure). At each step, every node can compute a local
weighted least-squares estimate, which converges to the global
maximum-likelihood solution. This scheme is robust to unreliable communication
links. We show that it works in a network with dynamically changing topology,
provided that the infinitely occurring communication graphs are jointly
connected.